Overview

Dataset statistics

Number of variables17
Number of observations11760
Missing cells1377
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory136.0 B

Variable types

Numeric9
Boolean3
Categorical5

Alerts

UserID is highly correlated with montly_avg_comment_on_company_pageHigh correlation
Yearly_avg_view_on_travel_page is highly correlated with Daily_Avg_mins_spend_on_traveling_pageHigh correlation
total_likes_on_outofstation_checkin_received is highly correlated with Daily_Avg_mins_spend_on_traveling_pageHigh correlation
montly_avg_comment_on_company_page is highly correlated with UserIDHigh correlation
Daily_Avg_mins_spend_on_traveling_page is highly correlated with Yearly_avg_view_on_travel_page and 1 other fieldsHigh correlation
Yearly_avg_view_on_travel_page is highly correlated with total_likes_on_outofstation_checkin_received and 1 other fieldsHigh correlation
total_likes_on_outofstation_checkin_received is highly correlated with Yearly_avg_view_on_travel_page and 1 other fieldsHigh correlation
Daily_Avg_mins_spend_on_traveling_page is highly correlated with Yearly_avg_view_on_travel_page and 1 other fieldsHigh correlation
UserID is highly correlated with yearly_avg_Outstation_checkins and 1 other fieldsHigh correlation
yearly_avg_Outstation_checkins is highly correlated with UserID and 1 other fieldsHigh correlation
preferred_location_type is highly correlated with UserID and 1 other fieldsHigh correlation
Yearly_avg_view_on_travel_page has 581 (4.9%) missing values Missing
total_likes_on_outstation_checkin_given has 381 (3.2%) missing values Missing
Yearly_avg_comment_on_travel_page has 206 (1.8%) missing values Missing
UserID is uniformly distributed Uniform
UserID has unique values Unique
week_since_last_outstation_checkin has 1032 (8.8%) zeros Zeros

Reproduction

Analysis started2022-05-01 03:47:13.604057
Analysis finished2022-05-01 03:47:35.845475
Duration22.24 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

UserID
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct11760
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1005880.5
Minimum1000001
Maximum1011760
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.0 KiB
2022-05-01T09:17:36.101509image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1000001
5-th percentile1000588.95
Q11002940.75
median1005880.5
Q31008820.25
95-th percentile1011172.05
Maximum1011760
Range11759
Interquartile range (IQR)5879.5

Descriptive statistics

Standard deviation3394.963917
Coefficient of variation (CV)0.003375116544
Kurtosis-1.2
Mean1005880.5
Median Absolute Deviation (MAD)2940
Skewness0
Sum1.182915468 × 1010
Variance11525780
MonotonicityStrictly increasing
2022-05-01T09:17:36.311514image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10014731
 
< 0.1%
10063021
 
< 0.1%
10104001
 
< 0.1%
10083531
 
< 0.1%
10022121
 
< 0.1%
10001651
 
< 0.1%
10063101
 
< 0.1%
10042631
 
< 0.1%
10104081
 
< 0.1%
10083611
 
< 0.1%
Other values (11750)11750
99.9%
ValueCountFrequency (%)
10000011
< 0.1%
10000021
< 0.1%
10000031
< 0.1%
10000041
< 0.1%
10000051
< 0.1%
10000061
< 0.1%
10000071
< 0.1%
10000081
< 0.1%
10000091
< 0.1%
10000101
< 0.1%
ValueCountFrequency (%)
10117601
< 0.1%
10117591
< 0.1%
10117581
< 0.1%
10117571
< 0.1%
10117561
< 0.1%
10117551
< 0.1%
10117541
< 0.1%
10117531
< 0.1%
10117521
< 0.1%
10117511
< 0.1%

Buy_ticket
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
False
9864 
True
1896 
ValueCountFrequency (%)
False9864
83.9%
True1896
 
16.1%
2022-05-01T09:17:36.462479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Yearly_avg_view_on_travel_page
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct331
Distinct (%)3.0%
Missing581
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean280.8308435
Minimum35
Maximum464
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.0 KiB
2022-05-01T09:17:36.596514image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile182
Q1232
median271
Q3324
95-th percentile411
Maximum464
Range429
Interquartile range (IQR)92

Descriptive statistics

Standard deviation68.18295849
Coefficient of variation (CV)0.2427901352
Kurtosis-0.2870008263
Mean280.8308435
Median Absolute Deviation (MAD)45
Skewness0.4144086403
Sum3139408
Variance4648.915828
MonotonicityNot monotonic
2022-05-01T09:17:36.775475image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
262190
 
1.6%
255186
 
1.6%
270179
 
1.5%
217165
 
1.4%
232160
 
1.4%
225148
 
1.3%
240142
 
1.2%
247139
 
1.2%
285136
 
1.2%
277133
 
1.1%
Other values (321)9601
81.6%
(Missing)581
 
4.9%
ValueCountFrequency (%)
354
< 0.1%
425
< 0.1%
1353
 
< 0.1%
1369
0.1%
1377
0.1%
1383
 
< 0.1%
1402
 
< 0.1%
1413
 
< 0.1%
1424
< 0.1%
1437
0.1%
ValueCountFrequency (%)
4641
 
< 0.1%
4631
 
< 0.1%
4622
 
< 0.1%
4612
 
< 0.1%
4603
< 0.1%
4592
 
< 0.1%
4581
 
< 0.1%
4573
< 0.1%
4565
< 0.1%
4557
0.1%

preferred_device
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
Mobile
10652 
Laptop
1108 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMobile
2nd rowMobile
3rd rowMobile
4th rowMobile
5th rowMobile

Common Values

ValueCountFrequency (%)
Mobile10652
90.6%
Laptop1108
 
9.4%

Length

2022-05-01T09:17:36.959475image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-01T09:17:37.067480image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
mobile10652
90.6%
laptop1108
 
9.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

total_likes_on_outstation_checkin_given
Real number (ℝ≥0)

MISSING

Distinct7888
Distinct (%)69.3%
Missing381
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean28170.48176
Minimum3570
Maximum252430
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.0 KiB
2022-05-01T09:17:37.197474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3570
5-th percentile5854
Q116380
median28076
Q340525
95-th percentile49945.5
Maximum252430
Range248860
Interquartile range (IQR)24145

Descriptive statistics

Standard deviation14385.03213
Coefficient of variation (CV)0.510642035
Kurtosis5.320921747
Mean28170.48176
Median Absolute Deviation (MAD)12034
Skewness0.4896375725
Sum320551912
Variance206929149.5
MonotonicityNot monotonic
2022-05-01T09:17:37.410471image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2418512
 
0.1%
1151511
 
0.1%
3787010
 
0.1%
1855010
 
0.1%
341959
 
0.1%
51459
 
0.1%
290159
 
0.1%
75958
 
0.1%
332508
 
0.1%
449058
 
0.1%
Other values (7878)11285
96.0%
(Missing)381
 
3.2%
ValueCountFrequency (%)
35702
< 0.1%
35771
< 0.1%
35781
< 0.1%
36052
< 0.1%
36111
< 0.1%
36141
< 0.1%
36181
< 0.1%
36201
< 0.1%
36211
< 0.1%
36311
< 0.1%
ValueCountFrequency (%)
2524301
< 0.1%
1524652
< 0.1%
1524301
< 0.1%
525121
< 0.1%
525091
< 0.1%
524981
< 0.1%
524951
< 0.1%
524871
< 0.1%
524791
< 0.1%
524741
< 0.1%

yearly_avg_Outstation_checkins
Categorical

HIGH CORRELATION

Distinct30
Distinct (%)0.3%
Missing75
Missing (%)0.6%
Memory size92.0 KiB
1
4543 
2
844 
10
682 
9
 
340
3
 
336
Other values (25)
4940 

Length

Max length2
Median length1
Mean length1.360462131
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
14543
38.6%
2844
 
7.2%
10682
 
5.8%
9340
 
2.9%
3336
 
2.9%
7336
 
2.9%
8320
 
2.7%
5261
 
2.2%
4256
 
2.2%
16255
 
2.2%
Other values (20)3512
29.9%

Length

2022-05-01T09:17:37.607512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
14543
38.9%
2844
 
7.2%
10682
 
5.8%
9340
 
2.9%
3336
 
2.9%
7336
 
2.9%
8320
 
2.7%
5261
 
2.2%
4256
 
2.2%
16255
 
2.2%
Other values (20)3512
30.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

member_in_family
Real number (ℝ≥0)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.921343537
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.0 KiB
2022-05-01T09:17:37.753513image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile4
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.04488284
Coefficient of variation (CV)0.3576720185
Kurtosis1.218338238
Mean2.921343537
Median Absolute Deviation (MAD)1
Skewness0.001204510761
Sum34355
Variance1.091780149
MonotonicityNot monotonic
2022-05-01T09:17:37.872476image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
34576
38.9%
43184
27.1%
22256
19.2%
11349
 
11.5%
5384
 
3.3%
1011
 
0.1%
ValueCountFrequency (%)
11349
 
11.5%
22256
19.2%
34576
38.9%
43184
27.1%
5384
 
3.3%
1011
 
0.1%
ValueCountFrequency (%)
1011
 
0.1%
5384
 
3.3%
43184
27.1%
34576
38.9%
22256
19.2%
11349
 
11.5%

preferred_location_type
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)0.1%
Missing31
Missing (%)0.3%
Memory size92.0 KiB
Beach
2424 
Financial
2409 
Historical site
1856 
Medical
1845 
Other
1386 
Other values (2)
1809 

Length

Max length15
Median length8
Mean length8.681302754
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFinancial
2nd rowFinancial
3rd rowOther
4th rowFinancial
5th rowMedical

Common Values

ValueCountFrequency (%)
Beach2424
20.6%
Financial2409
20.5%
Historical site1856
15.8%
Medical1845
15.7%
Other1386
11.8%
Entertainment1173
10.0%
Trekking636
 
5.4%
(Missing)31
 
0.3%

Length

2022-05-01T09:17:38.031514image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-01T09:17:38.150513image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
beach2424
17.8%
financial2409
17.7%
historical1856
13.7%
site1856
13.7%
medical1845
13.6%
other1386
10.2%
entertainment1173
8.6%
trekking636
 
4.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Yearly_avg_comment_on_travel_page
Real number (ℝ≥0)

MISSING

Distinct100
Distinct (%)0.9%
Missing206
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean74.79002943
Minimum3
Maximum815
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.0 KiB
2022-05-01T09:17:38.374513image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile41
Q157
median75
Q392
95-th percentile108
Maximum815
Range812
Interquartile range (IQR)35

Descriptive statistics

Standard deviation24.02664966
Coefficient of variation (CV)0.3212547159
Kurtosis134.7851038
Mean74.79002943
Median Absolute Deviation (MAD)18
Skewness4.868224985
Sum864124
Variance577.2798937
MonotonicityNot monotonic
2022-05-01T09:17:38.572481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96192
 
1.6%
66191
 
1.6%
90190
 
1.6%
56188
 
1.6%
80184
 
1.6%
72183
 
1.6%
95180
 
1.5%
92179
 
1.5%
88177
 
1.5%
79176
 
1.5%
Other values (90)9714
82.6%
(Missing)206
 
1.8%
ValueCountFrequency (%)
336
0.3%
3129
0.2%
3247
0.4%
3338
0.3%
3435
0.3%
3546
0.4%
3656
0.5%
3754
0.5%
3864
0.5%
3965
0.6%
ValueCountFrequency (%)
8151
 
< 0.1%
6851
 
< 0.1%
6151
 
< 0.1%
2151
 
< 0.1%
1257
0.1%
1243
 
< 0.1%
1238
0.1%
12210
0.1%
12111
0.1%
12010
0.1%

total_likes_on_outofstation_checkin_received
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct6288
Distinct (%)53.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6531.699065
Minimum1009
Maximum20065
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.0 KiB
2022-05-01T09:17:38.784512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1009
5-th percentile2132
Q12940.75
median4948
Q38393.25
95-th percentile17861
Maximum20065
Range19056
Interquartile range (IQR)5452.5

Descriptive statistics

Standard deviation4706.613785
Coefficient of variation (CV)0.7205803174
Kurtosis0.9987327559
Mean6531.699065
Median Absolute Deviation (MAD)2195
Skewness1.368578368
Sum76812781
Variance22152213.32
MonotonicityNot monotonic
2022-05-01T09:17:38.973512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
237712
 
0.1%
234211
 
0.1%
238011
 
0.1%
261010
 
0.1%
209610
 
0.1%
25709
 
0.1%
23879
 
0.1%
34529
 
0.1%
24049
 
0.1%
24379
 
0.1%
Other values (6278)11661
99.2%
ValueCountFrequency (%)
10092
< 0.1%
10141
< 0.1%
10171
< 0.1%
10501
< 0.1%
10512
< 0.1%
10522
< 0.1%
10551
< 0.1%
10581
< 0.1%
10601
< 0.1%
10612
< 0.1%
ValueCountFrequency (%)
200651
< 0.1%
200591
< 0.1%
200561
< 0.1%
200491
< 0.1%
200381
< 0.1%
200361
< 0.1%
200321
< 0.1%
200301
< 0.1%
200081
< 0.1%
200041
< 0.1%

week_since_last_outstation_checkin
Real number (ℝ≥0)

ZEROS

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.203571429
Minimum0
Maximum11
Zeros1032
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size92.0 KiB
2022-05-01T09:17:39.270475image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile9
Maximum11
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.616364893
Coefficient of variation (CV)0.8167025306
Kurtosis-0.03827306777
Mean3.203571429
Median Absolute Deviation (MAD)2
Skewness0.9153335743
Sum37674
Variance6.845365252
MonotonicityNot monotonic
2022-05-01T09:17:39.407515image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
13070
26.1%
31766
15.0%
21700
14.5%
41118
 
9.5%
01032
 
8.8%
5728
 
6.2%
6654
 
5.6%
7594
 
5.1%
9472
 
4.0%
8428
 
3.6%
Other values (2)198
 
1.7%
ValueCountFrequency (%)
01032
 
8.8%
13070
26.1%
21700
14.5%
31766
15.0%
41118
 
9.5%
5728
 
6.2%
6654
 
5.6%
7594
 
5.1%
8428
 
3.6%
9472
 
4.0%
ValueCountFrequency (%)
1160
 
0.5%
10138
 
1.2%
9472
 
4.0%
8428
 
3.6%
7594
 
5.1%
6654
 
5.6%
5728
6.2%
41118
9.5%
31766
15.0%
21700
14.5%
Distinct2
Distinct (%)< 0.1%
Missing103
Missing (%)0.9%
Memory size23.1 KiB
False
8360 
True
3297 
(Missing)
 
103
ValueCountFrequency (%)
False8360
71.1%
True3297
 
28.0%
(Missing)103
 
0.9%
2022-05-01T09:17:39.524475image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

montly_avg_comment_on_company_page
Real number (ℝ≥0)

HIGH CORRELATION

Distinct160
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.66156463
Minimum11
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.0 KiB
2022-05-01T09:17:39.655475image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile12
Q117
median22
Q327
95-th percentile36
Maximum500
Range489
Interquartile range (IQR)10

Descriptive statistics

Standard deviation48.66050382
Coefficient of variation (CV)1.6977616
Kurtosis59.66269923
Mean28.66156463
Median Absolute Deviation (MAD)5
Skewness7.684149905
Sum337060
Variance2367.844632
MonotonicityNot monotonic
2022-05-01T09:17:39.858475image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23673
 
5.7%
22653
 
5.6%
25609
 
5.2%
24605
 
5.1%
21594
 
5.1%
20588
 
5.0%
19574
 
4.9%
18573
 
4.9%
17524
 
4.5%
26505
 
4.3%
Other values (150)5862
49.8%
ValueCountFrequency (%)
11420
3.6%
12396
3.4%
13418
3.6%
14480
4.1%
15366
3.1%
16408
3.5%
17524
4.5%
18573
4.9%
19574
4.9%
20588
5.0%
ValueCountFrequency (%)
5001
< 0.1%
4991
< 0.1%
4971
< 0.1%
4911
< 0.1%
4902
< 0.1%
4881
< 0.1%
4871
< 0.1%
4861
< 0.1%
4851
< 0.1%
4842
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
False
9952 
True
1808 
ValueCountFrequency (%)
False9952
84.6%
True1808
 
15.4%
2022-05-01T09:17:39.994477image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
3
3672 
4
3456 
2
2424 
1
2208 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row4
3rd row2
4th row3
5th row4

Common Values

ValueCountFrequency (%)
33672
31.2%
43456
29.4%
22424
20.6%
12208
18.8%

Length

2022-05-01T09:17:40.117475image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-01T09:17:40.224520image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
33672
31.2%
43456
29.4%
22424
20.6%
12208
18.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

number_of_adults
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
0
5048 
1
4768 
2
1264 
3
680 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
05048
42.9%
14768
40.5%
21264
 
10.7%
3680
 
5.8%

Length

2022-05-01T09:17:40.372475image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-01T09:17:40.478515image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
05048
42.9%
14768
40.5%
21264
 
10.7%
3680
 
5.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Daily_Avg_mins_spend_on_traveling_page
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct52
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.81743197
Minimum0
Maximum270
Zeros46
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size92.0 KiB
2022-05-01T09:17:40.631514image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q18
median12
Q318
95-th percentile31
Maximum270
Range270
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.070656619
Coefficient of variation (CV)0.6564647206
Kurtosis93.94396127
Mean13.81743197
Median Absolute Deviation (MAD)5
Skewness4.480682458
Sum162493
Variance82.2768115
MonotonicityNot monotonic
2022-05-01T09:17:40.833479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101126
 
9.6%
9676
 
5.7%
8662
 
5.6%
6624
 
5.3%
7554
 
4.7%
13532
 
4.5%
11530
 
4.5%
12500
 
4.3%
14496
 
4.2%
15480
 
4.1%
Other values (42)5580
47.4%
ValueCountFrequency (%)
046
 
0.4%
1336
2.9%
2146
 
1.2%
3218
 
1.9%
4330
2.8%
5444
3.8%
6624
5.3%
7554
4.7%
8662
5.6%
9676
5.7%
ValueCountFrequency (%)
2701
 
< 0.1%
2351
 
< 0.1%
1701
 
< 0.1%
1351
 
< 0.1%
471
 
< 0.1%
463
 
< 0.1%
454
< 0.1%
448
0.1%
434
< 0.1%
426
0.1%

Interactions

2022-05-01T09:17:32.382427image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:17.919167image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:19.774463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:21.402463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:23.203428image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:24.972426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:26.900425image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:28.791427image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:30.627426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:32.583460image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:18.152261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:19.965429image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:21.615465image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:23.398464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:25.189422image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:27.115463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:28.990464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:30.834435image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:32.756466image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:18.339210image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:20.128461image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:21.795463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:23.569466image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:25.377466image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:27.309464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:29.168467image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:31.012427image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:32.959430image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:18.552326image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:20.316464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:21.996463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:23.871431image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:25.610426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:27.525476image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:29.363463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:31.210422image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:33.128465image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:18.728389image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:20.471425image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:22.169465image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:24.030426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:25.802472image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:27.708465image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:29.527426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:31.378426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:33.343466image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:18.949451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:20.668463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:22.398426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:24.232426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:26.033474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:27.936432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:29.850430image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:31.591465image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:33.554466image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:19.168426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:20.867426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:22.612463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:24.435469image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:26.264473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:28.160480image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:30.058464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:31.803425image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:33.743463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:19.365463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:21.047426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:22.804426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:24.618428image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:26.475473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:28.365424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:30.243427image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:31.995425image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:33.931431image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:19.573427image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:21.228464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:23.002465image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:24.797463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:26.687436image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:28.583466image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:30.434475image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-01T09:17:32.187425image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2022-05-01T09:17:41.022509image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-01T09:17:41.393479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-01T09:17:41.759479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-01T09:17:42.120512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-01T09:17:42.415474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-01T09:17:34.281462image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-01T09:17:34.881464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-01T09:17:35.318463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-05-01T09:17:35.581477image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

UserIDBuy_ticketYearly_avg_view_on_travel_pagepreferred_devicetotal_likes_on_outstation_checkin_givenyearly_avg_Outstation_checkinsmember_in_familypreferred_location_typeYearly_avg_comment_on_travel_pagetotal_likes_on_outofstation_checkin_receivedweek_since_last_outstation_checkinfollowing_company_pagemontly_avg_comment_on_company_pageworking_flagtravelling_network_ratingnumber_of_adultsDaily_Avg_mins_spend_on_traveling_page
01000001Yes307.0Mobile38570.012Financial94.059938Yes11No108
11000002No367.0Mobile9765.011Financial61.051301No23Yes4110
21000003Yes277.0Mobile48055.012Other92.020906Yes15No207
31000004No247.0Mobile48720.014Financial56.029091Yes11No308
41000005No202.0Mobile20685.011Medical40.034689No12No416
51000006No240.0Mobile35175.012Financial79.030680No13No308
61000007NoNaNMobile46340.013Medical81.026704Yes20Yes1312
71000008No225.0MobileNaN241Financial67.026931No22Yes211
81000009No285.0Mobile7560.0233Financial44.095260No21Yes2010
91000010No270.0Mobile45465.0273NaN94.052376No13No2217

Last rows

UserIDBuy_ticketYearly_avg_view_on_travel_pagepreferred_devicetotal_likes_on_outstation_checkin_givenyearly_avg_Outstation_checkinsmember_in_familypreferred_location_typeYearly_avg_comment_on_travel_pagetotal_likes_on_outofstation_checkin_receivedweek_since_last_outstation_checkinfollowing_company_pagemontly_avg_comment_on_company_pageworking_flagtravelling_network_ratingnumber_of_adultsDaily_Avg_mins_spend_on_traveling_page
117501011751No231.0Mobile16423.0284Historical site96.038451No26No2012
117511011752Yes383.0Mobile14399.0283Other58.0109106Yes28No2123
117521011753No302.0Mobile25317.0241Other79.0120930No24No1129
117531011754No247.0Mobile11418.053Historical site99.099831No28No2016
117541011755No210.0Mobile40886.053Other53.030242No32No4014
117551011756No279.0Laptop30987.0232Historical site58.026164No36No3123
117561011757No305.0Mobile21510.061Historical site55.0100414No30No1111
117571011758No214.0Mobile5478.043Beach103.062033Yes40Yes2112
117581011759No382.0Laptop35851.023Historical site83.054443No32No4020
117591011760No270.0Mobile22025.083Historical site104.044702No29No1014